direct_sampling.md

Direct Sampling Methods for SCSG

Here're some resources about Direct Sampling Methods for SCSG

Intros:

• This directory contains some direct sampling methods for SCSG, including data replay, random perturbation and clustering.

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Replay

Generating effective test cases for self-driving cars from police reports

paper link: here

citation:

@inproceedings{gambi2019generating,
  title={Generating effective test cases for self-driving cars from police reports},
  author={Gambi, Alessio and Huynh, Tri and Fraser, Gordon},
  booktitle={Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  pages={257--267},
  year={2019}
}

A risk-index based sampling method to generate scenarios for the evaluation of automated driving vehicle safety

paper link: here

citation:

@inproceedings{akagi2019risk,
  title={A risk-index based sampling method to generate scenarios for the evaluation of automated driving vehicle safety},
  author={Akagi, Yasuhiro and Kato, Ryosuke and Kitajima, Sou and Antona-Makoshi, Jacobo and Uchida, Nobuyuki},
  booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
  pages={667--672},
  year={2019},
  organization={IEEE}
}

An accelerated approach to safely and efficiently test pre-production autonomous vehicles on public streets

paper link: here

citation:

@inproceedings{arief2018accelerated,
  title={An accelerated approach to safely and efficiently test pre-production autonomous vehicles on public streets},
  author={Arief, Mansur and Glynn, Peter and Zhao, Ding},
  booktitle={2018 21st International Conference on Intelligent Transportation Systems (ITSC)},
  pages={2006--2011},
  year={2018},
  organization={IEEE}
}

Random Perturbation

Geosim: Realistic video simulation via geometry-aware composition for self-driving

paper link: here

citation:

@inproceedings{chen2021geosim,
  title={Geosim: Realistic video simulation via geometry-aware composition for self-driving},
  author={Chen, Yun and Rong, Frieda and Duggal, Shivam and Wang, Shenlong and Yan, Xinchen and Manivasagam, Sivabalan and Xue, Shangjie and Yumer, Ersin and Urtasun, Raquel},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={7230--7240},
  year={2021}
}

Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain

paper link: here

citation:

@article{scanlon2021waymo,
  title={Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain},
  author={Scanlon, John M and Kusano, Kristofer D and Daniel, Tom and Alderson, Christopher and Ogle, Alexander and Victor, Trent},
  journal={Accident Analysis \& Prevention},
  volume={163},
  pages={106454},
  year={2021},
  publisher={Elsevier}
}

Lidarsim: Realistic lidar simulation by leveraging the real world

paper link: here

citation:

@inproceedings{manivasagam2020lidarsim,
  title={Lidarsim: Realistic lidar simulation by leveraging the real world},
  author={Manivasagam, Sivabalan and Wang, Shenlong and Wong, Kelvin and Zeng, Wenyuan and Sazanovich, Mikita and Tan, Shuhan and Yang, Bin and Ma, Wei-Chiu and Urtasun, Raquel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11167--11176},
  year={2020}
}

Augmented LiDAR simulator for autonomous driving

paper link: here

citation:

@article{fang2020augmented,
  title={Augmented LiDAR simulator for autonomous driving},
  author={Fang, Jin and Zhou, Dingfu and Yan, Feilong and Zhao, Tongtong and Zhang, Feihu and Ma, Yu and Wang, Liang and Yang, Ruigang},
  journal={IEEE Robotics and Automation Letters},
  volume={5},
  number={2},
  pages={1931--1938},
  year={2020},
  publisher={IEEE}
}

Clustering

Scenario-based test reduction and prioritization for multi-module autonomous driving systems

paper link: here

citation:

@inproceedings{deng2022scenario,
  title={Scenario-based test reduction and prioritization for multi-module autonomous driving systems},
  author={Deng, Yao and Zheng, Xi and Zhang, Mengshi and Lou, Guannan and Zhang, Tianyi},
  booktitle={Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  pages={82--93},
  year={2022}
}

Extracting traffic primitives directly from naturalistically logged data for self-driving applications

paper link: here

citation:

@article{wang2018extracting,
  title={Extracting traffic primitives directly from naturalistically logged data for self-driving applications},
  author={Wang, Wenshuo and Zhao, Ding},
  journal={IEEE Robotics and Automation Letters},
  volume={3},
  number={2},
  pages={1223--1229},
  year={2018},
  publisher={IEEE}
}

Evolutionary

Generating critical test scenarios for automated vehicles with evolutionary algorithms

paper link: here

citation:

@inproceedings{klischat2019generating,
  title={Generating critical test scenarios for automated vehicles with evolutionary algorithms},
  author={Klischat, Moritz and Althoff, Matthias},
  booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)},
  pages={2352--2358},
  year={2019},
  organization={IEEE}
}

Testing vision-based control systems using learnable evolutionary algorithms

paper link: here

citation:

@inproceedings{abdessalem2018testing,
  title={Testing vision-based control systems using learnable evolutionary algorithms},
  author={Abdessalem, Raja Ben and Nejati, Shiva and Briand, Lionel C and Stifter, Thomas},
  booktitle={Proceedings of the 40th International Conference on Software Engineering},
  pages={1016--1026},
  year={2018}
}